12 citations · 33 across the 9 of their papers we have counts for
9 papers
Does Synthetic Data Make Large Language Models More Efficient?
Sia Gholami, Marwan Omar
Natural Language Processing (NLP) has undergone transformative changes with the advent of deep learning methodologies. One challenge persistently confronting researchers is the sca…
Can pruning make Large Language Models more efficient?
Sia Gholami, Marwan Omar
Transformer models have revolutionized natural language processing with their unparalleled ability to grasp complex contextual relationships. However, the vast number of parameters…
Can a student Large Language Model perform as well as it's teacher?
Sia Gholami, Marwan Omar
The burgeoning complexity of contemporary deep learning models, while achieving unparalleled accuracy, has inadvertently introduced deployment challenges in resource-constrained en…
Do Generative Large Language Models need billions of parameters?
Sia Gholami, Marwan Omar
This paper presents novel systems and methodologies for the development of efficient large language models (LLMs). It explores the trade-offs between model size, performance, and c…
Detecting software vulnerabilities using Language Models
Marwan Omar
Recently, deep learning techniques have garnered substantial attention for their ability to identify vulnerable code patterns accurately. However, current state-of-the-art deep lea…
RobustNLP: A Technique to Defend NLP Models Against Backdoor Attacks
Marwan Omar
As machine learning (ML) systems are being increasingly employed in the real world to handle sensitive tasks and make decisions in various fields, the security and privacy of those…